Assessment of APCC multimodel ensemble prediction in seasonal climate forecasting: Retrospective (1983–2003) and real‐time forecasts (2008–2013)

Min, Young-Mi; Kryjov, Vladimir N.; Oh, Seokhoon

doi:10.1002/2014jd022230

Cited by 58 publications

(50 citation statements)

References 67 publications

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“…Consistent with the negative regression, the PDF for El Niño is shifted to the left of the climatological PDF. The results are also consistent with an array of previous studies that seasonal predictability and forecastability is high (low) in tropical (extratropical) latitudes and is a consequence of well separated (intermingled) climatological and ENSO PDFs (Peng et al 2000;Min et al 2014). 5, however, there is a considerable overlap between the climatological and El Niño PDFs, and this feature is very similar to that in Fig.…”

Section: Validation Based On Gcm Simulationssupporting

confidence: 91%

Inherent Predictability, Requirements on the Ensemble Size, and Complementarity

Kumar

Chen

2015

Monthly Weather Review

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Faced with the scenario when prediction skill is low, particularly in conjunction with long-range predictions, a commonly proposed solution is that an increase in ensemble size will rectify the issue of low skill. Although it is well known that an increase in ensemble size does lead to an increase in prediction skill, the general scope of this supposition, however, is that low prediction skill is not a consequence of constraints imposed by inherent predictability limits, but an artifact of small ensemble sizes, and further, increases in ensemble sizes (that are often limited by computational resources) are the major bottlenecks for improving long-range predictions. In proposing that larger ensemble sizes will remedy the issue of low skill, a fact that is not well appreciated is that for scenarios with high inherent predictability, a small ensemble size is sufficient to realize high predictability, while for scenarios with low inherent predictability, much larger ensemble sizes are needed to realize low predictability. In other words, requirements on ensemble size (to realize the inherent predictability) and inherent predictability are complementary variables. A perceived need for larger ensembles, therefore, may also imply the presence of low predictability.

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Section: Validation Based On Gcm Simulationssupporting

confidence: 91%

Inherent Predictability, Requirements on the Ensemble Size, and Complementarity

Kumar

Chen

2015

Monthly Weather Review

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“…Even though the range of the correlation coefficients is similar to most of the state-of-the-art models and the multimodel ensemble predictions for the central Asia region (Min et al, 2014;Kim et al, 2016), this result indicates that the skill of seasonal predictions over the region is less reliable and not enough to meet the demand of the people and decision-makers of the region. Therefore, the aforementioned statistical approach using the ANN is necessary for better and horizontally finer prediction over the Mongolia region.…”

Section: Hindcast Of Pnu Cgcmmentioning

confidence: 78%

“…In fact, even most of the state-of-the-art models and the multi-model ensemble prediction systems have relatively poor predictability in high-latitude landlocked regions such as the Eurasian continent (Min et al, 2014;Kim et al, 2016), compared to ocean areas. From the outputs of PNU CGCM, TI mdl and SHI mdl are acquired as the large-scale climate indices.…”

Section: Discussionmentioning

confidence: 99%

Seasonal prediction of high‐resolution temperature at 2‐m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network

Gerelchuluun

Ahn

2018

Intl Journal of Climatology

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The hindcast data of Pusan National University coupled general circulation model (PNU CGCM), a participant model of the Asia‐Pacific Economic Cooperation Climate Center (APCC) Multi‐Model Ensemble Climate Prediction System, and August–October sea‐surface temperature (SST) in the northern Barents–Kara Sea (BKI) and the sea‐ice extent (SIE) in the Chukchi Sea (East Siberian Sea index [ESI]) are used for predicting 20 × 20‐km‐resolution anomalous surface air temperature at 2‐m height (aT2m) over Mongolia for boreal winter. For this purpose, area‐averaged surface air temperature (TI) and sea‐level pressure (SLP) over Mongolia are defined. Then four large‐scale indices, TImdl and SHImdl obtained from PNU CGCM, and TIMLR and SHIMLR obtained from multiple linear regressions on BKI and ESI, are incorporated using the artificial neural network (ANN) method for the prediction and statistical downscaling to obtain the monthly and seasonal 20 × 20‐km‐resolution aT2m over Mongolia in winter. An additional statistical method, which uses BKI and ESI as predictors of TI and SHI together with dynamic prediction by the CGCM, is used because of the relatively low skill of seasonal predictions by most of the state‐of‐the‐art models and the multi‐model ensemble systems over high‐latitude landlocked Eurasian regions such as Mongolia. The results show that the predictabilities of monthly and seasonal 20 × 20‐km‐resolution aT2m over Mongolia in winter are improved by applying ANN to both statistical and dynamical predictions compared to utilizing only dynamic prediction. The predictability gained by the proposed method is also demonstrated by the probabilistic forecast implying that the method forecasts aT2m over Mongolia in winter reasonably well.

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“…A real‐time forecast assessment of APCC multimodel ensemble prediction based on multiple regression was performed for the time period 2008–2013 by Min et al . (). Over India, the Extended Range Forecast System is focused on monthly to seasonal scale prediction of precipitation (Mohanty et al ., ).…”

Section: Resultsmentioning

confidence: 99%

Assessing the performance of bias correction approaches for correcting monthly precipitation over India through coupled models

Singh

Sahoo

Nair

et al. 2017

Meteorological Applications

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The objective of the present study was to investigate the inter‐annual variation and error structure in the prediction of monthly precipitation through two global coupled models, the National Centers for Environmental Prediction Climate Forecast System version 2 (CFSv2) and the Geophysical Fluid Dynamics Laboratory model. In view of the consistent systematic bias (dry bias during summer monsoon months and wet bias during pre‐monsoon months in CFSv2) a requirement to correct the inherent error is inevitable. For this purpose, a few bias correction methods, standardization−reconstruction (Z), quantile−quantile mapping (QQ) and nonlinear transformation (NL_Zi), are explored. The methods are applied to the outputs of the dynamic models and the efficiency is examined through different statistical skill measures. A maximum error reduction is noticed for March, July, September and December. A decreasing tendency for rainfall in July is represented by the raw model and its biased counterpart. The observed probability is noticed to be overestimated (underestimated) corresponding to below normal (above normal) precipitation in the raw model. The varying relationship between monthly precipitation and the NINO3.4 index might be a reason for misleading prediction during extreme years. Among the bias correction methods, NL_Zi showed maximum improvement in terms of predicting the precipitation amount and probability distribution all through the year irrespective of the selection of the coupled model.

show abstract

Assessment of APCC multimodel ensemble prediction in seasonal climate forecasting: Retrospective (1983–2003) and real‐time forecasts (2008–2013)

Cited by 58 publications

References 67 publications

Inherent Predictability, Requirements on the Ensemble Size, and Complementarity

Inherent Predictability, Requirements on the Ensemble Size, and Complementarity

Seasonal prediction of high‐resolution temperature at 2‐m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network

Assessing the performance of bias correction approaches for correcting monthly precipitation over India through coupled models

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